This data notebook is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
If you want to cite the method/model please use:
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.
If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/
If you wish to re-use material from this data notebook please cite it as:
Ben Anderson (2021) Data notebook: Simulating a local emissions levy to fund local energy effiency retrofit: Winchester. University of Southampton, United Kingdom
License: CC-BY
Share, adapt, give attribution.
This data notebook estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.
It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.
Key results:
This data notebook estimates a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator.
The model applies carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. It then sums these values to given an overall levy revenue estimate for the area in the case study.
The data notebook then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
Finally the data notebook compares the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so the data notebook also analyses the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this area level analysis uses mean emissions per household. It will therefore not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy values that might be expected.
NB: no maps in the interests of speed
The model uses a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions.
All analysis is at LSOA level. Cautions on inference from area level data apply.
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
region | nLSOAs | mean_KgCo2ePerCap | sd_KgCo2ePerCap |
South East | 64 | 10,435.3 | 3,333.1 |
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 001B Wonston and Micheldever 1141 1221 693
## 2: Winchester 014C Denmead 1020 1112 731
## 3: Winchester 003A St Barnabas 896 896 442
## 4: Winchester 004E Alresford and Itchen Valley 844 876 534
## 5: Winchester 013D Southwick and Wickham 824 1304 901
## 6: Winchester 007A St Michael 814 1138 884
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Winchester 013D Southwick and Wickham 824 1304 901
## 2: Winchester 001B Wonston and Micheldever 1141 1221 693
## 3: Winchester 007A St Michael 814 1138 884
## 4: Winchester 014C Denmead 1020 1112 731
## 5: Winchester 014A Southwick and Wickham 726 977 744
## 6: Winchester 004A Upper Meon Valley 48 973 505
LSOA11NM | WD18NM | nGasMeters | nElecMeters | epc_total |
Winchester 001B | Wonston and Micheldever | 1,141 | 1,221 | 693 |
Winchester 014C | Denmead | 1,020 | 1,112 | 731 |
Winchester 003A | St Barnabas | 896 | 896 | 442 |
Winchester 004E | Alresford and Itchen Valley | 844 | 876 | 534 |
Winchester 013D | Southwick and Wickham | 824 | 1,304 | 901 |
Winchester 007A | St Michael | 814 | 1,138 | 884 |
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 64 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 24933.52 | 8685.63 | 11275.19 | 18731.61 | 24005.72 | 31004.27 | 47031.62 | ▆▇▆▂▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2453.54 | 611.06 | 102.68 | 2169.00 | 2454.65 | 2840.90 | 3783.79 | ▁▁▅▇▂ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1132.35 | 254.86 | 778.32 | 956.46 | 1081.99 | 1238.38 | 1896.98 | ▇▇▂▂▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3585.90 | 662.13 | 1999.65 | 3107.95 | 3567.58 | 3977.05 | 5225.86 | ▂▅▇▅▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 243.91 | 374.48 | 17.96 | 52.53 | 90.85 | 222.03 | 2048.92 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3829.80 | 706.14 | 2416.16 | 3388.92 | 3855.76 | 4315.99 | 5354.20 | ▅▇▇▇▃ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 3023.43 | 930.43 | 1178.16 | 2275.68 | 3082.84 | 3832.39 | 4673.38 | ▅▇▇▇▇ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 390.30 | 390.33 | 55.18 | 139.57 | 292.94 | 522.73 | 2093.34 | ▇▂▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 3413.73 | 1132.67 | 1252.96 | 2563.84 | 3379.91 | 4292.45 | 6447.09 | ▃▇▆▃▁ |
Examine patterns of per dwelling emissions for sense.
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.1: Scatter of LSOA level all consumption emissions per dwelling against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -6.1744, df = 62, p-value = 5.634e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7492275 -0.4376306
## sample estimates:
## cor
## -0.6170604
## Total emissions per dwelling (LSOA level) summary
## LSOA11CD WD18NM IMD_Decile_label All_Tco2e_per_dw
## Length:64 Length:64 10 (10% least deprived):25 Min. :11.28
## Class :character Class :character 9 :13 1st Qu.:18.73
## Mode :character Mode :character 7 : 7 Median :24.01
## 8 : 7 Mean :24.93
## 5 : 4 3rd Qu.:31.00
## 6 : 4 Max. :47.03
## (Other) : 4
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01023241 | Wonston and Micheldever | 9 | 47.0 |
E01023242 | Badger Farm and Oliver's Battery | 10 (10% least deprived) | 45.2 |
E01023268 | St Paul | 10 (10% least deprived) | 44.3 |
E01032859 | Whiteley and Shedfield | 9 | 38.1 |
E01023270 | St Paul | 10 (10% least deprived) | 37.4 |
E01023271 | Whiteley and Shedfield | 7 | 37.2 |
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01023261 | St Luke | 4 | 11.3 |
E01023221 | Bishop's Waltham | 7 | 11.3 |
E01023250 | St Barnabas | 6 | 11.6 |
E01023252 | St Michael | 7 | 11.8 |
E01023256 | St Bartholomew | 3 | 11.8 |
E01023285 | Southwick and Wickham | 5 | 12.5 |
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 102.7 2169.0 2454.7 2453.5 2840.9 3783.8
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -5.42, df = 62, p-value = 1.036e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7133838 -0.3732082
## sample estimates:
## cor
## -0.5670019
Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -0.96731, df = 62, p-value = 0.3371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.357040 0.127705
## sample estimates:
## cor
## -0.1219319
Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -0.96731, df = 62, p-value = 0.3371
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.357040 0.127705
## sample estimates:
## cor
## -0.1219319
RUC11 | mean_gas_kgco2e | mean_elec_kgco2e | mean_other_energy_kgco2e |
Rural town and fringe | 2,454.4 | 1,090.9 | 183.5 |
Rural village and dispersed | 2,276.4 | 1,543.2 | 824.7 |
Urban city and town | 2,521.5 | 1,001.4 | 60.0 |
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 5.8031, df = 62, p-value = 2.389e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4067955 0.7322961
## sample estimates:
## cor
## 0.5932803
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 5.8533, df = 62, p-value = 1.968e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4110607 0.7346623
## sample estimates:
## cor
## 0.5965891
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
How does the correlation look now?
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -4.9256, df = 62, p-value = 6.568e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6866312 -0.3271725
## sample estimates:
## cor
## -0.5303315
RUC11 | mean_car_kgco2e | mean_van_kgco2e |
Rural town and fringe | 3,373.7 | 396.9 |
Rural village and dispersed | 3,881.8 | 805.6 |
Urban city and town | 2,453.9 | 225.1 |
Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.69702, df = 62, p-value = 0.4884
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1611247 0.3269007
## sample estimates:
## cor
## 0.08817693
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
Table 4.6 below shows the overall £ GBP total for the case study area in £M under Scenario 1.
nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
64 | 286.0 | 28.7 | 13.6 |
region | nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
South East | 64 | 286.0 | 28.7 | 13.6 |
Figure 4.7: Proportion of total emissions due to gas & electricity use by region covered
The table below shows the mean per dwelling value rounded to the nearest £10.
All_emissions | Gas | Electricity | Gas + Electricity |
6,108.7 | 601.1 | 277.4 | 878.5 |
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.9: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2762 4589 5881 6109 7596 11523
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01023241 | Winchester 003B | Wonston and Micheldever | 47.0 | 11,522.7 |
E01023242 | Winchester 009B | Badger Farm and Oliver's Battery | 45.2 | 11,073.0 |
E01023268 | Winchester 005D | St Paul | 44.3 | 10,858.2 |
E01032859 | Winchester 013F | Whiteley and Shedfield | 38.1 | 9,331.2 |
E01023270 | Winchester 005F | St Paul | 37.4 | 9,153.4 |
E01023271 | Winchester 013B | Whiteley and Shedfield | 37.2 | 9,110.5 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01023261 | Winchester 008B | St Luke | 11.3 | 2,762.4 |
E01023221 | Winchester 012B | Bishop's Waltham | 11.3 | 2,770.9 |
E01023250 | Winchester 005C | St Barnabas | 11.6 | 2,852.8 |
E01023252 | Winchester 007A | St Michael | 11.8 | 2,884.9 |
E01023256 | Winchester 006C | St Bartholomew | 11.8 | 2,889.9 |
E01023285 | Winchester 013E | Southwick and Wickham | 12.5 | 3,052.6 |
Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.11: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 25.16 531.41 601.39 601.12 696.02 927.03
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01023230 | Winchester 010D | Badger Farm and Oliver's Battery | 3.8 | 927.0 |
E01023241 | Winchester 003B | Wonston and Micheldever | 3.7 | 904.9 |
E01023226 | Winchester 010A | Colden Common and Twyford | 3.4 | 824.6 |
E01023268 | Winchester 005D | St Paul | 3.4 | 823.9 |
E01023244 | Winchester 009D | Badger Farm and Oliver's Battery | 3.3 | 810.7 |
E01023266 | Winchester 008D | St Paul | 3.2 | 786.8 |
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01023252 | Winchester 007A | St Michael | 1.7 | 412.5 |
E01023251 | Winchester 006A | St Michael | 1.7 | 404.5 |
E01023224 | Winchester 014A | Southwick and Wickham | 1.5 | 374.6 |
E01023243 | Winchester 009C | Badger Farm and Oliver's Battery | 1.5 | 360.5 |
E01023280 | Winchester 004D | Alresford and Itchen Valley | 1.3 | 308.4 |
E01023225 | Winchester 004A | Upper Meon Valley | 0.1 | 25.2 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.13: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 190.7 234.3 265.1 277.4 303.4 464.8
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01023225 | Winchester 004A | Upper Meon Valley | 1.9 | 464.8 |
E01023236 | Winchester 002A | Alresford and Itchen Valley | 1.8 | 434.7 |
E01023229 | Winchester 009A | Badger Farm and Oliver's Battery | 1.7 | 427.5 |
E01023245 | Winchester 013A | Whiteley and Shedfield | 1.6 | 402.5 |
E01023280 | Winchester 004D | Alresford and Itchen Valley | 1.6 | 402.1 |
E01023274 | Winchester 003E | Wonston and Micheldever | 1.6 | 382.7 |
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01023243 | Winchester 009C | Badger Farm and Oliver's Battery | 0.9 | 211.9 |
E01023251 | Winchester 006A | St Michael | 0.9 | 209.4 |
E01032860 | Winchester 013G | Whiteley and Shedfield | 0.8 | 207.2 |
E01023237 | Winchester 002B | The Worthys | 0.8 | 206.7 |
E01023261 | Winchester 008B | St Luke | 0.8 | 195.8 |
E01023260 | Winchester 008A | St Luke | 0.8 | 190.7 |
Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 489.9 761.4 874.1 878.5 974.4 1280.3
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 11275.19 18731.61 24005.72 31004.27 47031.62
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Table: (#tab:estimateAnnualLevyScenario2Total)Data summary
| Name | …[] |
| Number of rows | 64 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 24.93 | 8.69 | 11.28 | 18.73 | 24.01 | 31.00 | 47.03 | ▆▇▆▂▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 4415.52 | 2638.62 | 1375.57 | 2300.11 | 3599.31 | 6145.88 | 12027.92 | ▇▃▂▂▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 3134190.38 | 1511997.02 | 817314.60 | 1823951.87 | 2962663.14 | 4118309.20 | 6086127.04 | ▇▆▆▃▅ |
nLSOAs | sum_total_sc1 | sum_total_sc2 |
64 | 286.0 | 200.6 |
Figure 4.16 compares the % £ levy under each scenario for all consumption contributed by LSOAs in each IMD decile.
Figure 4.16: Comparing £ levy under each scenario by IMD decile - all consumption emissions
Figure 4.17 compares the £ levy under each scenario for all consumption.
Figure 4.17: Comparing £ levy under each scenario - all consumption emissions
## [1] 17.84687
## [1] 9.672869
nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP |
64 | 200.6 | 17.8 | 9.7 |
region | nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP | sumPop |
South East | 64 | 200.6 | 17.8 | 9.7 | 114,310 |
Figure 4.18: Contribution to sum levy £ GBP by source
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 227.0 354.0 407.0 438.6 493.2 901.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 479.0 649.2 727.0 756.1 865.2 1304.0
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table 4.13 reports total retofit costs.
## To retrofit D-E (£m)
## [1] 317.2953
## Number of dwellings: 23857
## To retrofit F-G (£m)
## [1] 50.83369
## Number of dwellings: 1897
## To retrofit D-G (£m)
## [1] 368.129
## To retrofit D-G (mean per dwelling)
## [1] 14179.92
meanPerLSOA_GBPm | total_GBPm |
5.8 | 368.1 |
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.19 shows the LSOA level retofit costs per dwelling by IMD decile.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.19: LSOA level retofit costs per dwelling by IMD score
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.20 shows years to pay under Scenario 1 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.209 1.887 2.442 2.632 3.097 5.120
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.20: Years to pay under Scenario 1 (all em issions)
## Median years: 2.44
Figure 4.21 shows years to pay under Scenario 1 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.15 14.31 16.12 16.75 18.33 35.11
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.21: Years to pay under Scenario 1 (energy emissions)
## Median years: 16.12
Figure 4.22 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 4.22: Year 1 outcome if levy is shared equally (all emissions levy)
LSOA11CD | LSOA11NM | WD18NM | retrofitSum | yearsToPay | epc_D_pc | epc_E_pc | epc_F_pc | epc_G_pc |
E01023225 | Winchester 004A | Upper Meon Valley | 13,987,308.5 | 35.1 | 0.3 | 0.3 | 0.2 | 0.0 |
E01023236 | Winchester 002A | Alresford and Itchen Valley | 10,788,912.2 | 15.2 | 0.4 | 0.3 | 0.1 | 0.0 |
E01023245 | Winchester 013A | Whiteley and Shedfield | 9,834,178.5 | 14.4 | 0.4 | 0.2 | 0.1 | 0.0 |
E01023287 | Winchester 001B | Wonston and Micheldever | 9,399,233.3 | 14.3 | 0.5 | 0.1 | 0.0 | 0.0 |
E01023229 | Winchester 009A | Badger Farm and Oliver's Battery | 9,126,368.5 | 13.3 | 0.4 | 0.2 | 0.1 | 0.0 |
E01023284 | Winchester 013D | Southwick and Wickham | 8,734,773.8 | 19.0 | 0.3 | 0.1 | 0.0 | 0.0 |
E01023240 | Winchester 003A | St Barnabas | 8,118,124.9 | 14.8 | 0.5 | 0.1 | 0.0 | 0.0 |
E01023280 | Winchester 004D | Alresford and Itchen Valley | 7,835,526.7 | 21.8 | 0.4 | 0.3 | 0.1 | 0.0 |
E01023252 | Winchester 007A | St Michael | 7,522,514.7 | 22.3 | 0.3 | 0.1 | 0.0 | 0.0 |
E01023264 | Winchester 007D | St Michael | 7,336,981.1 | 16.5 | 0.3 | 0.3 | 0.0 | 0.0 |
Figure 4.23 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 4.23: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
Figure 4.24 shows years to pay under Scenario 2 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.179 2.341 4.057 4.472 5.950 10.282
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.24: Years to pay under Scenario 2 (all em issions)
## Median years: 4.06
Figure 4.25 shows years to pay under Scenario 2 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.15 14.31 16.12 16.75 18.33 35.11
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.25: Years to pay under Scenario 2 (energy emissions)
Figure 4.26 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 4.26: Year 1 outcome if levy is shared equally (all emissions levy)
Figure 4.27 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 4.27: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades…
Figure 4.28 compares pay-back times for the two scenarios - who does the rising block tariff help?
Figure 4.28: Comparing pay-back times across scenarios
I don’t know if this will work…
## Doesn't